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Impara Challenge: Bag of Words | Basic Text Models
Introduction to NLP

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Challenge: Bag of Words

Compito

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You have a text corpus stored in corpus variable. Your task is to display the vector for the 'graphic design' bigram in a BoW model. To do this:

  1. Import the CountVectorizer class to create a BoW model.
  2. Instantiate the CountVectorizer class as count_vectorizer, configuring it for a frequency-based model that includes both unigrams and bigrams.
  3. Use the appropriate method of count_vectorizer to generate a BoW matrix from the 'Document' column in the corpus and store the result in bow_matrix.
  4. Convert bow_matrix to a dense array and create a DataFrame from it, setting the unique features (unigrams and bigrams) as its columns. Store the result in the bow_df variable.
  5. Display the vector for 'graphic design' bigram as an array.

Soluzione

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Sezione 3. Capitolo 5
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book
Challenge: Bag of Words

Compito

Swipe to start coding

You have a text corpus stored in corpus variable. Your task is to display the vector for the 'graphic design' bigram in a BoW model. To do this:

  1. Import the CountVectorizer class to create a BoW model.
  2. Instantiate the CountVectorizer class as count_vectorizer, configuring it for a frequency-based model that includes both unigrams and bigrams.
  3. Use the appropriate method of count_vectorizer to generate a BoW matrix from the 'Document' column in the corpus and store the result in bow_matrix.
  4. Convert bow_matrix to a dense array and create a DataFrame from it, setting the unique features (unigrams and bigrams) as its columns. Store the result in the bow_df variable.
  5. Display the vector for 'graphic design' bigram as an array.

Soluzione

Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

close

Awesome!

Completion rate improved to 3.45

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